Perils of Data-based Birding

As a birder-scientist, not only do I have the joys of exploring farmlands and forests for novel encounters, but I also have the “joys” of exploring fields…. as in data fields.

As part of my PhD program, to become more knowledgeable about birds, I have to spend more time away from real birds (much of the current knowledge transfer system is based on similar logic…e.g. to improve learning we should spend less time learning and more time testing). For the spring semester, I opted to do a rotation in the lab of Dr. Gareth Russell.

The Russell Lab does fascinating work looking at animal choice and response to their environment by modeling behavior and movement. Current and recent work has followed Grizzly Bears in Alaska, Big Horn Sheep in California, Baleen Whales social networks in the Atlantic Ocean, Elephants in Africa. The modeling element comes in when scientist try to interpret why the animals made the choices they did. What elements of the environment most resonated with the animal causing its behaviour?

Turns out since you can’t actually ask the animals, you can use a computer to figure it out.

For my project, I can share the details later, I obtained USGS banding records for songbirds along the east coast to determine movements over time. My goal was to use this data to answer questions about their migratory movements in the fall.

As it goes with a venture into any new area, there’s a steep learning curve as you get to know the landscape. You have weird experiences such as when I reloaded the data set and every bird name involving a color (say yellow warbler and white-throated sparrow) became {#*code#) warbler and (#&code#(-throated sparrow.

Twitter Screen Capture.

So after months of learning to program in Mathematica (mixed success), I managed to create a map! This beautiful rendered map shows 50+ years worth of records where in the birds presented:

were banded and recaptured after July 1,

were banded and recaptured in the same year,

were recaptured in a new location.

Mathematica output: Apparently one bird didn’t get the memo and went west, not south. H1: That bird can’t read a compass. H2: That bird is a rebellious juvenile.

However, as beautifully colored as it is and as important as it looks, it’s only ~800 pieces of data across 44 species and 50+ years, not enough for a solid analysis. Back to the drawing board.

When after significant hours of effort, you’re still coming up with nothing, you doubt yourself, even when other people have faith. “There aren’t any birds here!”

Twitter Screen Capture.

Then you do something really basic, like identifying an American Robin, or writing a function which figures out how many days into a year it is.

Twitter Screen Capture.

Now you know there are birds there, and you’re just not finding them. You feel conflicted about that.

Three weeks to go and we’re still “exploring the data”. The sun is nearly setting.

Mathematica output: Tree swallow presence (blue) and absence (white) data with years as the x axis and days into the year as the y axis.

So, finally we made a graph. This graph, for Tree Swallows, shows presence (blue) and absence (white) data with years as the x axis and days into the year as the y axis. Think January at the bottom and December at the top.

Now that I’d found one bird surely I could find them all, but could I do it by family?

Yes… Probably… How hard could it be?… Maybe… If I had more time… Impossible… Wait, what?… No… Maybe… Nope… Did I get it?

After a very dark night:

Mathematica output: 10 families of songbirds tracked over the years for arrivals and departures. years are represented on the x axis and days into the year as the y axis.

I actually succeeded as the sun rose. Talk about symbolism! I’ll have a chance to share this in my next meeting on Friday. Hopefully we can call this success.